Inhalt

German:
Bei der Informationsextraktion und dem Text Mining geht es um das Aufspüren und Strukturieren relevanter Informationseinheiten aus einer Menge von unstrukturierten oder semi-strukturierten Texten. Wichtige Teilbereiche hierbei sind z.B. Extraktion von Termen und Ontologien, Topic Detection, Named Entity Erkennung, Extraktion von Templates und Klassifikation von Dokumenten. In den letzten Jahren werden in diesen verschiedenen Teilbereichen verstärkt, maschinelle Lernverfahren entwickelt, um eine möglichst automatische Anpassung der Basistechnologien an neue Anwendungen und Wissensbereiche zu erreichen.
Das Seminar fängt mit einer kurzen Einfürung in die Gebiete Informationsextraktion und Text Mining an, die auch eine Abgrenzung zu verwandten Themen wie z.B. Information Retrieval and Question Answering Systems umfasst. Das Ziel dieses Seminars ist es dann, verschiedene aktuelle maschinelle Lernverfahren für Informationsextraktion und Text Mining zu diskutieren und zu vergleichen.
 

English:
Information extraction and text mining is concerned with the detection and normalization of relevant information from a set of unstructured
or semi-structured NL texts. Important subtasks are here, e.g., the extraction of terms and ontologies, topic detection, named entity recognition,
extraction of templates and text categorization. In recent years more and more approaches based on Machine Learning are used in order to
achieve a high degree of automatic adaptation towards new application and knowledge areas.
This seminar starts with a short introduction into the fields of information extraction and text mining, and a brief comparision with other
research areas like information retrieval and question answering systems. The main objective is then, to discuss and compare the different
learning approaches currently developed in information extraction and text mining.

Literatur
 

Einige kompakte Einführungen zum Thema Informationsextraktion und Text Mining finden sich in:

• G. Neumann: Informationsextraktion. In Klabunde et al (eds): Computerlinguistik und Sprachtechnologie - Eine Einführung. Spektrum Akademischer Verlag, Heidelberg (Buch erscheint Ende 2001)
• Douglas Appelt: Introduction to Information Extraction Technology
• Marti A. Hearst: Text Data Mining, Issues, Techniques, and Relation to Information Access

Einführungsvorträge

• 18. April       Überblick in Informationsextraktion       Günter Neumann
• 25. April       Überblick in Textmining [.ppt][.htm]  Jakub Piskorski und Feiyu Xu

Vorläufige Themenliste und Zeitplan

• 2. Mai        Wissensbasierte IE-Systeme: FASTUS, GATE, DFKI-SMES-Technologien
•     Vorträge von Neumann[.ppt][.htm], Piskorski [.ppt] [.htm] und Xu [.ppt][.htm]
•      Literatur
• Homepage of FASTUS
• Hobbs, Appelt, Bear, Israel, Kameyama, Stickel and Tyson, FASTUS: A Cascaded Finite-State Transducer for Extracting Information from Natural-Language Text, in Roche and Schabes, eds., Finite State Devices for Natural Language Processing, MIT Press, Cambridge MA, 1996.
• Homepages of Gate
• Publications of Gate

DFKI-SMES-Technologien
• G. Neumann, R. Backofen, J. Baur, M. Becker, C. Braun: An Information Extraction Core System for Real World German Text Processing. In proceedings of ANLP-1997, Washington, DC, pages 209-216
• J. Piskorski and G. Neumann: An Intelligent Text Extraction and Navigation System In proceedings of 6th International Conference on Computer-Assisted Information Retrieval (RIAO-2000), Paris, 2000
• G. Neumann, C. Braun and J. Piskorski: A Divide-and-Conquer Strategy for Shallow Parsing of German Free Texts In proceedings of ANLP-2000, Seattle, Washington, pages 239-246
• G. Neumann and J. Piskorski: A Shallow Text Processing Core Engine. Under review by the Journal of Computational Intelligence.

 
• 9. Mai       Template Merging
• Vortrag von Tatjana Scheffler [.ps.gz]
•  Literatur
• Homepage of FASTUS
• Hobbs, Appelt, Bear, Israel, Kameyama, Stickel and Tyson, FASTUS: A Cascaded Finite-State Transducer for Extracting Information from Natural-Language Text, in Roche and Schabes, eds., Finite State Devices for Natural Language Processing, MIT Press, Cambridge MA, 1996.
• Douglas Appelt: Introduction to Information Extraction Technology
• Andrew Kehler:  Learning Embedded Discourse Mechanisms for Information Extraction,

in the Proceedings of the AAAI Spring Symposium on Applying Machine Learning to
Discourse Processing, Stanford, CA, March 1998.
 
 
 
• 16. Mai     Induktive Lernverfahren: Supervised/Unsupervised Template Learning
• Literatur
• Ion Muslea: Extraction Patterns for Information Extractionn Tasks: A Survey
• Riloff's Publication Page of Information Extraction
• Roman Yangarber's Publication Page (only papers in 2000)

 
• 23. Mai    Named Entity Recognition (Decision trees, HMMs, Maximum Entropy Modeling)
• Vortrag von Stephan Lesch[.ppt][.htm]
• Literatur
• A. Gallippi, Learning to Recognize Names Across Languages. In Proceedings of the Sixteenth International Conference on Computational Linguistics. Copenhagen, Denmark. August, 1996
• Bikel, Miller, Schwartz and Weischedel, Nymble: a High-Performance Learning Name-finder,In proceedings of ANLP-1997, Washington, DC, pages 195-201
• A. Borthwick, A Maximum Entropy Approach to Named Entity Recognition, Ph.D. (1999)  New York University.  Department of Computer Science, Courant Institute.

 

• 30. Mai    Information Wrapping --- Lernen aus Semi-Strukturierten Dokumenten
• Vortrag von Ulf Knoblich [.ps.gz]
• Literatur
• D. Freitag, Information extraction from HTML: application of a general machine learning approach, In Proceedings of AAAI-98
• D. Freitag and N. Kushmerick, Boosted wrapper induction, In Proceedings of AAAI-2000.
• Hsin-Hsi Chen, Shih-Chung Tsai, Jin-He Tsai, Mining Tables from Large Scale HTML Texts, In Proceedings of Coling 2000, Saarbrücken, Germany, 2000.

 
• 6. Juni      Classification
• Vortrag von Sven Schmeier. [.ppt] [.htm][animation]
• Literatur
• Yiming Yang's Publication Page
• Yiming Yang and Xin Liu A re-examination of text categorization methods. Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 1999, pp 42--49.

 
• Yiming Yang An Evaluation of statistical approaches to text categorization. Journal of Information Retrieval,

    1999, Vol 1, No. 1/2, pp 67--88.
• Support Vector Machine
• G. Neumann and S. Schmeier: Combining Shallow Text Processing and Machine Learning in Real World Applications  In Proceedings of the IJCAI-99 workshop on Machine Learning for Information Filtering, Stockholm, Sweden, 1999.

 

• 13. Juni    Clustering
•   Literatur
• A Comparison of Document Clustering Techniques. Michael Steinbach,

George Karypis and Vipin Kumar
• Clustering Full Text Documents, Joel D. Martin, Workshop on Data Engineering for Inductive Learning IJCAI-95, Montreal (Canada), August 20, 1995
• Lightweight Document Clustering, Sholom Weiss, Brian White, Chid Apte
• Zamir O., Etzioni O.: Web Document Clustering: A Feasibility Demonstration. In:

SIGIR'98, Melbourne, Australia.
• http://dewey.yonsei.ac.kr/memexlee/links/clustering.htm

 

 
 
 

• 20. Juni    Topic detection
• Vortrag: Johanna Völker [.ppt.zip]
• Literatur
• Topic Detection and Tracking (TDT)
• K. Seymore and R. Rosenfeld,  Large-scale Topic Detection and Language Model Adaptation.

 Tech. report CMU-CS-97-152, Computer Science Department, Carnegie Mellon University, June, 1997.
• Yiming Yang, Jaime Carbonell, Ralf Brown, Thom Pierce, Brian T. Archibald, Xin Liu, Learning Approaches for Detecting and Tracking News Events (pdf.gz). IEEE Intelligent Systems: Special Issue on Applications of Intelligent Information Retrieval,Vol. 14(4), pp32-43, July/August 1999.
• Allan, J., Carbonell, J.G., Doddington, G., Yamron, J. and Yang Y.,  Topic Detection and Tracking Pilot Study  Final Report, Proceedings of the Broadcast News Transcription and Understranding Workshop (Sponsored by DARPA), Feb. 1998.
•  L. A. Mather, J. Note: Discovering Encyclopedic Structure and Topics in Text

KDD-2000 Workshop on Text Mining
 
 
 
• 27. Juni    Extraktion und Induktion von Ontologien und Lexikalisch Semantischen Relationen
• Vortrag: Claudia Vispi [.ppt]
• Literatur
• CMU World Wide Knowledge Base (Web->KB) project,  CS Department, Carnegie Mellon University, Pittsburgh
•  M. Craven, D. DiPasquo, D. Freitag, A. McCallum, T. Mitchell, K. Nigam and S. Slattery. Learning to Extract Symbolic Knowledge from the World Wide Web, Proceedings of the 15th National Conference on Artificial Intelligence (AAAI-98).
• Finkelstein-Landau and Morin, Extracting Semantic Relationships between Terms: Supervised vs. Unsupervised Methods. In Actes, International Workshop on Ontological Engineering on the Global Information Infrastructure, pages 71-80, Dagstuhl Castle, Germany, 1999.
• A. Maedche and S. Staab. Discovering Conceptual Relations from Text. In Proceedings of the 14th European Conference on Artificial Intelligence, Berlin, August 21-25, 2000.

 
 
• 4. Juli      Chinese Information Extraction (Gastvortrag von Prof. Yao von der Shanghai Jiao Tong Universität)
•   Vortrag: Tianfang Yao [.ppt]

Scheine
Vortrag und Hausarbeit
________________________________________________________________________________________

E-mail: neumann@dfki.de

Last modified: 19.04.2001